code: def _get_attention_mask_for_prompts(
      input_ids: List[List[int]], max_prompt_len: int
  ) -> List[List[int]]:
      attention_mask = [
          [0] * (max_prompt_len - len(prompt)) + [1] * len(prompt)
          for prompt in input_ids
      ]
      return attention_mask
  
file_path: BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models/bigdl_mistral.py
imports: import torch
  from torch import nn
  from transformers import AutoTokenizer, PreTrainedTokenizerBase, LlamaConfig
  from typing import Optional, Tuple, List, Type, Dict
  from bigdl.llm.vllm.sequence import SequenceOutputs, SequenceGroupMetadata
  from bigdl.llm.vllm.model_executor.layers.bigdl_sampler import BigDLSampler
  from bigdl.llm.vllm.model_executor.models.bigdl_model import BigDLModelForCausalLM
  from bigdl.llm.vllm.logger import init_logger
  import math
  import time
  from bigdl.llm.vllm.model_executor.input_metadata import InputMetadata
  from transformers.generation.logits_process import LogitsProcessorList, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper
label_desc: Use image node_id 2 for calling a global function with example usage: _get_attention_mask_for_prompts(input_ids, max_prompt_len) and returns: attention_mask